4.7 Article

Laser-Induced Fluorescence for Monitoring Environmental Contamination and Stress in the Moss Thuidium plicatile

期刊

PLANTS-BASEL
卷 12, 期 17, 页码 -

出版社

MDPI
DOI: 10.3390/plants12173124

关键词

bryophyte; metal contamination; environmental stress; metal detection; laser-induced fluorescence; image processing

向作者/读者索取更多资源

Researchers have successfully identified different metals and environmental stressors using laser-induced fluorescence (LIF) imaging analysis of moss. The analysis of RGB values in the images allows for the detection of contaminated samples, distinguishing them from photoperiod and other environmental factors.
The ability to detect, measure, and locate the source of contaminants, especially heavy metals and radionuclides, is of ongoing interest. A common tool for contaminant identification and bioremediation is vegetation that can accumulate and indicate recent and historic pollution. However, large-scale sampling can be costly and labor-intensive. Hence, non-invasive in-situ techniques such as laser-induced fluorescence (LIF) are becoming useful and effective ways to observe the health of plants through the excitation of organic molecules, e.g., chlorophyll. The technique presented utilizes images collected of LIF in moss to identify different metals and environmental stressors. Analysis through image processing of LIF response was key to identifying Cu, Zn, Pb, and a mixture of the metals at nmol/cm(2 )levels. Specifically, the RGB values from each image were used to create density histograms of each color channel's relative pixel abundance at each decimal code value. These histograms were then used to compare color shifts linked to the successful identification of contaminated moss samples. Photoperiod and extraneous environmental stressors had minimal impact on the histogram color shift compared to metals and presented with a response that differentiated them from metal contamination.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据